At the end of the course, students are expected to: - know the state of the art ML and DL model and tools in different application domains - be able to develop working ML and DL solutions for complex real-world applications in biology and industry - experience joint development of projects in a group of engineers

The students are expected to already have - solid programming skills, experience with software development - good knowledge of probability and statistics, linear algebra, calculus - basic knowledge of object-oriented programming (Python) - basic knowledge of shallow and deep machine learning models and programming frameworks

ML framework in real-world applications: from datum to knowledge [15h] - Overview of ML taxonomy and applications, data types and issues - From sensor to ML-ready data - Data preprocessing and balancing - Feature extraction and selection - From classic ML to DL: recaps of domain-specific models and tools - ML and DL for image analysis and understanding - ML and DL for sequence analysis - Issues and challenges in real-world applications Encompassing the issues of classic DL [15h] - Uncertainty modelling and data cleaning - Generative modelling for data-critical applications - Self-supervised and semi-supervised learning - Neuromorphic computing and engineering Application domains and hands-on case-studies [30h] - ML for Biology and Medicine - Biological image analysis - Genetic sequence analysis - ML for Industry - Human activity recognition - Anomaly detection, predictive maintenance, production and logistics optimization - Additive Manufacturing: quality assurance and process optimization

The course has an hands-on learning approach, and mainly targets students of the computer engineering area, with solid programming skills and prior experience with software development.

The course consists of 30h of frontal lectures and 30h of laboratories. The so-called laboratories include - tutorials on specific ML frameworks - practical exercises on the topics covered by the lectures - project-related activities on several case-studies from biology and industry, that will be conducted with the continuous support of the teachers.

There is no specific reference text book. Reading material will be made available through portale della didattica. It will include: - slides used during the lectures - research papers about the topics covered during the course - occasionally, pointers to videos/online tutorials

Lecture slides; Lab exercises; Lab exercises with solutions; Video lectures (current year);

Exam: Compulsory oral exam; Group project;

The exam consists of a group (2-4 people) project addressing a practical case study in line with the topic of the course, agreed-upon with the teacher. Each group will be asked to submit the developed code as well as a report (8-10 pages in double-column format) with a research paper-like description of their work, including: challenges and motivations, state of the art, proposed solution and discussion of the obtained results. The project will be then presented and discussed by all the group's components through an oral session (either in person or via web-conferencing). After the ppt group presentation (about 20 minutes), each group member will be asked questions about the project and her/his individual contribution to the group activity, as well as general questions on the topics covered by the course. The final grade of each student will consist of: - evaluation of the group project (same for all the group members, 70%). This evaluation takes into account: the complexity of the addressed problem; the originality and richness of the proposed solution; the methodological and technical correctness of the solution; the completeness and quality of the report; the completeness and quality of the oral presentation - evaluation of the individual oral discussion (30%). This evaluation takes into account: the quality of the individual presentation; the individual effort and contribution to the group activity; the correctness of the answers to theoretical and technical questions; the individual communication skills. Both the components of the evaluation are given in a 0 to 32 range. In case the final grade is above 30, the outcome is 30 con lode.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.